This project brings together a strong interdisciplinary team of investigators to develop powerful image information processing and database tools for the exponentially growing bio-molecular image data, thus enabling a new generation of bio-image informatics. Contemporary microscopic techniques, including immunofluorescence microscopy, electron microscopy, GFP visualization and atomic force microscopy have contributed enormously to our current understanding of molecular cell biology in its many forms. Individual research labs often generate hundreds or even thousands of images weekly. Unfortunately, most of these images are analyzed, labeled and archived manually. Further, the vast majority of these images never get published, despite the fact that many of them would be valuable to other researchers. The problems inherent in manual labeling and archiving, together with the lack of a central and searchable repository for images of all sorts (analogous to DNA sequence databases) is a major impediment to progress in essentially all fields under the general description of molecular and cellular biology. These problems also represent a major impediment to progress in the emerging area of bio-image informatics, and tools for organizing and information processing of such data are urgently needed. By coupling recent advances in image processing, pattern recognition and data mining, with the enormous amount of data that is being generated in bio- molecular imaging, significant progress can be made in our understanding of various cellular and subcelluar processes. [unreadable] [unreadable] The primary goal of an STTR effor is that of technology transfer and commericialization, and the proposed goals are aimed at bringing to a scientist's desktop powerful image informatics tools. By making such methods easily accesible, we expect to facilitate rapid advances in computational biology that is increasingly dependent on large scale imaging technologies. To this end, we are devloping a simple yet powerful database system that would help scientists analyze and share their image databases. The Phase I effort, a joint venture between Mayachitram Inc. and biology professors Fisher and Feinstein at the University of California, Santa Barbara, has led to an initial prototype system with over one thousand images in the database. With this database we were able to successfully test some of the core image processing and database technologies, with immunoflourescence iamges from the biologists. [unreadable] [unreadable] This STTR Phase II proposal targets this critical need in continuing to develop a simple and flexible image database system that would help the scientists analyze and mine large data collections. [unreadable] [unreadable] [unreadable]